Results 11 to 20 of about 16,797 (216)
Bayesian error propagation for neural-net based parameter inference
The Open Journal of Astrophysics, 2022 Neural nets have become popular to accelerate parameter inferences, especially for the upcoming generation of galaxy surveys in cosmology. As neural nets are approximative by nature, a recurrent question has been how to propagate the neural net's ...
Daniela Grandón, Elena Sellentin
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Network anomaly detection using deep learning techniques
CAAI Transactions on Intelligence Technology, 2022 Convolutional neural networks (CNNs) are the specific architecture of feed‐forward artificial neural networks. It is the de‐facto standard for various operations in machine learning and computer vision.
Mohammad Kazim Hooshmand +1 more
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INSM1, a Novel Biomarker for Detection of Neuroendocrine Neoplasms: Cytopathologists’ View
Diagnostics, 2021 Background: Insulinoma-associated protein 1 (INSM1) has been considered as a novel immunostain for neuroendocrine tumors (NETs) and is hypothesized to be more reliable than first-generation NET biomarkers, such as CGA (chromogranin A), SYP (synaptophysin)
Zahra Maleki +5 more
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Side channel attacks for architecture extraction of neural networks
CAAI Transactions on Intelligence Technology, 2021 Side channel attacks (SCAs) on neural networks (NNs) are particularly efficient for retrieving secret information from NNs. We differentiate multiple types of threat scenarios regarding what kind of information is available before the attack and its ...
Hervé Chabanne +3 more
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Compressing fully connected layers of deep neural networks using permuted features
IET Computers & Digital Techniques, 2023 Modern deep neural networks typically have some fully connected layers at the final classification stages. These stages have large memory requirements that can be expensive on resource‐constrained embedded devices and also consume significant energy just
Dara Nagaraju, Nitin Chandrachoodan
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Deep Net Tree Structure for Balance of Capacity and Approximation Ability
Frontiers in Applied Mathematics and Statistics, 2019 Deep learning has been successfully used in various applications including image classification, natural language processing and game theory. The heart of deep learning is to adopt deep neural networks (deep nets for short) with certain structures to ...
Charles K. Chui +4 more
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IET Intelligent Transport Systems, 2023 Rail transit has many advantages, such as large passenger capacity, convenience, safety, and environmental protection, making it the preferred travel mode for most passengers.
Xuanrong Zhang +3 more
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SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition
IET Image Processing, 2023 Although most state‐of‐the‐art action recognition models have adopted a two‐stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models.
Zhaoqilin Yang +4 more
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Temporal Modeling of Neural Net Input/Output Behaviors: The Case of XOR
Systems, 2017 In the context of the modeling and simulation of neural nets, we formulate definitions for the behavioral realization of memoryless functions. The definitions of realization are substantively different for deterministic and stochastic systems constructed
Bernard P. Zeigler, Alexandre Muzy
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Quantum speed-up in global optimization of binary neural nets
New Journal of Physics, 2021 The performance of a neural network (NN) for a given task is largely determined by the initial calibration of the network parameters. Yet, it has been shown that the calibration, also referred to as training, is generally NP-complete.
Yidong Liao +3 more
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